1. Field of the Invention
The present invention relates to visualizing scattered data points using a computer display.
2. Related Art
Computer visualization tools are called upon to handle ever increasing amounts of data. Conventional scatter plots visually represent multivariate data points as graphical glyphs plotted along one, two, or three axes. Each data point has one or more data attributes, also called variables. These data attributes can be numerical or categorical. Each axis can represent a different data attribute. Additional data attributes can be represented by varying the color or size of the glyphs.
Problems are encountered in visualizing scattered data when the number of data points is large. In general, each data point in a conventional scatter plot is represented by a corresponding glyph. As the number of scattered data points increases, more glyphs crowd a scatter plot display. The time it takes to render each glyph increases. The time it takes to build and display a scatter plot can become too long, thereby, precluding interactive, on-the-fly rendering of scattered data. Occlusion can also occur as data points in the foreground of a scatter plot hide data points behind them. A serious problem occurs when many data points occupy the same location.
To illustrate the above problem, consider a two-dimensional scatter plot containing millions of data points. It takes a very long time for a graphics processor to draw millions of glyphs covering all these data points. If each data point is represented by a single pixel on the screen, then there will be many overlapping data points. Only the data point for a glyph which is drawn last for a given pixel location will be seen.
The same problems occur in three-dimensional scatter plots where three-dimensional (3-D) glyphs (e.g., cubes, spheres, etc.) are used to represent data points. These 3-D glyphs are plotted with respect to three scatter plot axes. Rendering such a 3-D scatter plot for large numbers of data points can take a long time, as many glyphs must be processed. Moreover, if there are many data points to be covered, glyphs in the foreground occlude those in the back. Also, data is hidden when the data points are clustered together. There is no easy way to examine data inside a cluster.
What is needed is a data visualization tool that visually approximates a scatter plot when a large number of data points needs to be drawn. Further, what is needed is a visualization tool that provides for the smooth animation of a scatter plot along one or more additional dimensions.